FeedbackBypass: A new Approach to Interactive Similarity Query Processing

Publication

Publication

Presented at the
International Conference on Very Large Databases, Rome, Italy

In recent years, several methods have been proposed for implementing interactive similarity queries on multimedia databases. Common to all these methods is the idea to exploit user feedback in order to progressively adjust the query parameters and to eventually converge to an "optimal" parameter setting. However, all these methods also share the drawback to "forget" user preferences across multiple query sessions, thus requiring the feedback loop to be restarted for every new query, i.e. using default parameter values. Not only is this proceeding frustrating from the user's point of view but it also constitutes a significant waste of system resources.
In this paper we present FeedbackBypass, a new approach to interactive similarity query processing. It complements the role of relevance feedback engines by storing and maintaining the query parameters determined with feedback loops over time, using a wavelet-based data structure (the Simplex Tree). For each query, a favorable set of query parameters can be determined and used to either "bypass" the feedback loop completely for already-seen queries, or to start the search process from a near-optimal configuration.
FeedbackBypass can be combined well with all state-of-the-art relevance feedback techniques working in high-dimensional vector spaces. Its storage requirements scale linearly with the dimensionality of the query space, thus making even sophisticated query spaces amenable. Experimental results demonstrate both the effectiveness and efficiency of our technique.